code: def test_ray_remote_args(shutdown_only):
      ray.init(num_cpus=4, resources={"custom_resource": 4})
      register_ray()
  
      assert ray.available_resources().get("custom_resource", 0) == 4
  
      def check_resource():
          assert ray.available_resources().get("custom_resource", 0) < 4
  
      with joblib.parallel_backend(
          "ray", ray_remote_args={"resources": {"custom_resource": 1}}
      ):
          joblib.Parallel()(
              joblib.delayed(check_resource)() for i in range(8)
          )
  
imports: import joblib
  import sys
  import time
  import os
  import pytest
  from unittest import mock
  import pickle
  import numpy
  from sklearn.datasets import load_digits, load_iris
  from sklearn.model_selection import RandomizedSearchCV
  from sklearn.ensemble import ExtraTreesClassifier
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.kernel_approximation import Nystroem
  from sklearn.kernel_approximation import RBFSampler
  from sklearn.pipeline import make_pipeline
  from sklearn.svm import LinearSVC, SVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.linear_model import LogisticRegression
  from sklearn.neural_network import MLPClassifier
  from sklearn.model_selection import cross_val_score
  from ray.util.joblib import register_ray
  import ray
label_desc: Use image node_id 7 for calling a global function with example usage: test_ray_remote_args(shutdown_only) without return types